Before we get started, let’s load the data that summarizes all species.
Here’s a phylogeny of the sphenomorphines (outgroups not shown). This includes OTUs only.
## [1] "RSS: 3.19829753114225e-08"
There are 298 putative OTUs in sphenomorphines; this tree samples 248 of them.
Of these OTUs, we sampled 104 for gene flow.
Note that we need to get data for Eremiascincus_douglasi_1; Eremiascincus_isolepis_1; Eremiascincus_richardsonii_2. Right now, these tips have been pasted into their species complex at the halfway point of the terminal branch length.
Across these OTUs, we sampled 1147 individuals for ddRAD data.
The basic approach we used:
We have included detailed summary reports for each OTU in Dropbox/Sphenomorphine_Gene_Flow/figures/maps.pdf. Note that I have trouble opening it in Preview so you might try Adobe
Each page shows:
the Fst estimates shown with respect to geographic distance and the inferred slope
also shown for OTUs where we sampled 6 or more individuals is how jackknifing individuals influences Fst estimates
Take homes:
We estimated genetic differentiation between individuals using:
Note that there is no strong theoretical framework for using mtDNA dxy and nDNA dxy to look at differentiation across the landscape, so we focus on Fst here. However, if we compare pairwise estimates across all individuals, these different approaches to measuring differentiation are all pretty correlated.
But particularly in dxy mtDNA & dxy nDNA see some differences that are likely because of cytonuclear introgression.
While not a key question in this work, much of the genetic differentiation literature these days is centered around IBD (isolation-by-distance) vs. IBE (isolation-by-environment). To help put our work in context of this, we looked at how much of the variation in pairwise-Fst approaches is explained by geographic vs. environmental distance.
This plot only shows significant results, and uses Wang (2012)’s approach to multiple-matrix regression.
Gray bars are environment, black bars show geographic distance – total length of line reflects how much of Fst variation is explained by the two measures. In general, we can explain about half of the pattern of genetic differentiation across OTUs.
There is a decent amount of variation in slopes inferred across OTUs. This shows all slopes, including the few which were non-significant. Note that it is also not very normally distributed, so all subsequent analyses will use log(slope).
Another way to look at this is how these slopes change across genera. Here, showing the same results as above but for only genera in which we calculated slopes for 3 or more OTUs.
If we look at these slopes across the phylogeny, we can recapitulate what we saw in the boxplot: there is phylogenetic signal in the pattern of slope. need to add legend Indeed, the pattern of differentiation does show phylogenetic signal: lambda is 0.2985306 and the pvalue for this is 0.0125744.
What explains why there is variation in IBD slopes?
Genetic differentiation (as measured here) is a function of effective population size (Ne) and migration (m). Further, arguments centered around IBD suggest that the more environmental heterogeneity across a range, the less differentiation we would expect. So, based on this, we include the following factors:
First, we looked at correlation among these factors in our model. Including factors with too much colinearity can make model fitting do weird things.
Then, we test if these factors show phylogenetic signal. Note that within population pi (mean_pi) didn’t show phylogenetic signal when measured just across Ctenotus. All our factors (but for ninds) show strong patterns of phylogenetic signal. [1] “x has no names; assuming x is in the same order as tree\(tip.label" [1] "x has no names; assuming x is in the same order as tree\)tip.label” [1] “x has no names; assuming x is in the same order as tree\(tip.label" [1] "some data in x given as 'NA', dropping corresponding species from tree" [1] "x has no names; assuming x is in the same order as tree\)tip.label” [1] “some data in x given as ‘NA’, dropping corresponding species from tree” [1] “x has no names; assuming x is in the same order as tree\(tip.label" [1] "x has no names; assuming x is in the same order as tree\)tip.label” [1] “x has no names; assuming x is in the same order as tree\(tip.label" [1] "x has no names; assuming x is in the same order as tree\)tip.label”
| factor | lambda | pvalue |
|---|---|---|
| ninds | 0.1761827 | 0.3328809 |
| mean_pi | 0.8076407 | 0.0000000 |
| svl | 1.0508282 | 0.0000000 |
| shank | 1.0872383 | 0.0000000 |
| range_size | 0.8722269 | 0.0000037 |
| lat_midpoint | 0.9682147 | 0.0000020 |
| elev_range | 0.9364051 | 0.0000000 |
| PC1_range | 0.9039572 | 0.0000003 |
Now, we do our model fitting exercise where we infer which of these variables best predict variation in slope variation across OTUs. This is the same model-averaging approach used in Singhal et al 2017 and first described by Burnham and Anderson 2002.
We took the natural log of two IV variables (ninds, range size) and the dependent variable (slope) based on visual inspection of histograms.
## Model 100 of 255 done.
## Model 200 of 255 done.
These results suggest that the most important factors explaining variation in IBD slopes are:
shank length: as expected, as an OTU has more substantial legs, differentiation decreases – a naive explanation: more leggy lizards move more and therefore, higher dispersal leads to reduced differentiation
elevational range: this supports the IBE hypothesis that we see more differentiation with more heterogenetiy (i.e., a positive correlation)
I do not trust this result; see below
We can look at these significant results one by one.
This analysis is because I think it will be of interest to people in the community. We do not see strong correlations here, though there are correlations. Note (again) that there is no theoretical foundation for looking at mtDNA and nDNA dxy differentiation across space.
One of the classic results in this work comes from Dan’s work (Rabosky et al 2007, Rabosky et al 2014) showing that Ctenotus & Lerista have much higher rates of speciation than the rest of the clade. I am repeating these results here using our tree which includes putative new OTUs. For now, just using the diversification rate (DR) statistic used by Jetz et al 2012.
As shown earlier, we still see that Ctenotus & Lerista have higher rates of speciation than the rest of the clade.
Here, we show the phylogeny with variance in speciation rates again, showing only those tips that we sampled.
Finally, we test for if variation in genetic differentiation can explain variation in speciation rates, using a PGLS.
We tested a model in which we took the log of the differentiation slope and one in which we did not, and in neither, does the differentiation slope significantly predict the DR speciation rate. For the untransformed data, the p-value is 0.9035116 and for the transformed data, it is 0.9913201.
Our data provide no evidence that variation in rates of genetic differentiation explain variation in speciation rates.